{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "t97KLQhBTg3p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "from sklearn.datasets import load_boston\n",
        "\n",
        "sns.set(style=\"ticks\", color_codes=True)\n",
        "plt.rcParams['figure.figsize'] = (8,5)\n",
        "plt.rcParams['figure.dpi'] = 150"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ-GtRS8UCGh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loading the data\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/PacktPublishing/hands-on-exploratory-data-analysis-with-python/master/Chapter%209/Boston.csv\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhfObCNjnWV8",
        "colab_type": "text"
      },
      "source": [
        "This returns all keys and values as python dictionary. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Khssn3W2nuWi",
        "colab_type": "code",
        "outputId": "d96c6311-3281-46a6-d5ab-9632b6ae3fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(df.keys())"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['CRIM', ' ZN ', 'INDUS ', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
            "       'TAX', 'PTRATIO', 'LSTAT', 'MEDV'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOovQHPIn72c",
        "colab_type": "text"
      },
      "source": [
        "Here you can view the data set description using boston.DESCR, which describes each feature in the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpWCCVyjptuc",
        "colab_type": "code",
        "outputId": "aa352a1b-890b-4bac-9b80-61c657145b7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(df.columns)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['CRIM', ' ZN ', 'INDUS ', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
            "       'TAX', 'PTRATIO', 'LSTAT', 'MEDV'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRQKm0Top6PX",
        "colab_type": "text"
      },
      "source": [
        "So MEDV is our target variable, which we need to predict and remaining are our features.\n",
        "\n",
        "Now that we got our data loaded, let's get our data frame ready quickly and work ahead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6DjBavcnR2u",
        "colab_type": "code",
        "outputId": "e802f94e-e74e-4480-ae01-b8a0332eab9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# df = pd.DataFrame(boston.data,columns=boston.feature_names)\n",
        "df.head()\n",
        "# print the columns present in the dataset\n",
        "print(df.columns)\n",
        "# print the top 5 rows in the dataset\n",
        "print(df.head()) "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['CRIM', ' ZN ', 'INDUS ', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
            "       'TAX', 'PTRATIO', 'LSTAT', 'MEDV'],\n",
            "      dtype='object')\n",
            "      CRIM   ZN   INDUS   CHAS    NOX  ...  RAD  TAX  PTRATIO  LSTAT  MEDV\n",
            "0  0.00632  18.0    2.31     0  0.538  ...    1  296     15.3   4.98  24.0\n",
            "1  0.02731   0.0    7.07     0  0.469  ...    2  242     17.8   9.14  21.6\n",
            "2  0.02729   0.0    7.07     0  0.469  ...    2  242     17.8   4.03  34.7\n",
            "3  0.03237   0.0    2.18     0  0.458  ...    3  222     18.7   2.94  33.4\n",
            "4  0.06905   0.0    2.18     0  0.458  ...    3  222     18.7   5.33  36.2\n",
            "\n",
            "[5 rows x 13 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTj35MN28R7M",
        "colab_type": "text"
      },
      "source": [
        "In new overall dataframe let’s check if we have any missing values in the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5NcjkgEx3Qy",
        "colab_type": "code",
        "outputId": "67c28878-0888-459e-dc26-9964e5a50d11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              " ZN        0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "LSTAT      0\n",
              "MEDV       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnGvx39WiTg3",
        "colab_type": "code",
        "outputId": "2816d8a0-5127-41fe-cef3-3716de105e79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        }
      },
      "source": [
        "sns.distplot(df['MEDV'])\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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isaVXA0L6DAAAgBFEMAAAMMWhUxf9j7PT4mS32UysZuwJbEBY09iqjk6fidUA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pF6Mo+0U+ddmcDqmM0mrkyIYEjfDlx9eZT+wIuIiIhIvRgOg+ZJC9BsCQhPEtHGn8fGX8Gk0T1Z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\niAdTYkBERERERETEg/1/o5jDNteru8cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1200x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuYk2mowjCaJ",
        "colab_type": "code",
        "outputId": "5319b6e2-6e93-441b-adf0-ed46959107a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        }
      },
      "source": [
        "sns.lmplot(x = 'RM', y = 'MEDV', data = df)\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f6d1a71ea58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k37LhQrz7dy+HYTrIG81vpzc0WsBXdxzAa0PVR5PfePWFuOdt60MzfIfBmoiI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4PkOp4gYhJsv3pfZ8Ktw/OuiWHZdirmDHvh4B8MH3Xo0PvGsw0eRAzU5PSFOW/1UeVrR3Owax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\n",
            "text/plain": [
              "<Figure size 750x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHy0X8Pq2eBb",
        "colab_type": "code",
        "outputId": "c77709dd-9bf4-404a-af37-79350b205f7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        }
      },
      "source": [
        "#plotting heatmap for overall data set\n",
        "sns.heatmap(df.corr(), square=True, cmap='RdYlGn')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1a68b588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AwFnNnz9f+fn5Cg8PL7Laq169eoqJidHjjz+uxMREHTlyREFBQWXOd/z4cZ06dUqS9Pbb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cOHIQgC/P39i32OIAiYMWMGjhw5gsOHD0v+3fbo0SMolcoSnfPy95qxFF0aZmZmaN++Pdq3b4/0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\nIiIiIiIiMiAWXURERERERAb0P26QSRFYS7kSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x750 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hM8jDm6d69sD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preparing the data\n",
        "X = df[['RM']]\n",
        "y = df[['MEDV']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVh7mKbm_XaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fP99jBCE7Gg",
        "colab_type": "code",
        "outputId": "3eb10103-2366-4b69-f040-0a8e4ab88069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Training a Linear Regression Model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "\n",
        "# Fitting the training data to our model\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-niW85_VHmKv",
        "colab_type": "text"
      },
      "source": [
        "**Model Evaluation**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "769nv_oDJSVl",
        "colab_type": "code",
        "outputId": "50c8beea-0cfb-4783-eb7c-778106a18550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#check prediction score\n",
        "regressor.score(X_test, y_test)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5383003344910231"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJxiuJ7NHk3k",
        "colab_type": "code",
        "outputId": "c316251c-aaab-4d8b-a0dd-0de7a36d7e28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# predict the y values\n",
        "y_pred=regressor.predict(X_test)\n",
        "# a data frame with actual and predicted values of y\n",
        "evaluate = pd.DataFrame({'Actual': y_test.values.flatten(), 'Predicted': y_pred.flatten()})\n",
        "evaluate.head(10)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.4</td>\n",
              "      <td>25.153909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.1</td>\n",
              "      <td>26.773693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.5</td>\n",
              "      <td>22.284072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.6</td>\n",
              "      <td>27.997335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.6</td>\n",
              "      <td>14.484456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.3</td>\n",
              "      <td>23.569336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>50.0</td>\n",
              "      <td>32.839084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14.3</td>\n",
              "      <td>16.535597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>20.7</td>\n",
              "      <td>19.026896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37.6</td>\n",
              "      <td>37.689635</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0    28.4  25.153909\n",
              "1    31.1  26.773693\n",
              "2    23.5  22.284072\n",
              "3    26.6  27.997335\n",
              "4    19.6  14.484456\n",
              "5    14.3  23.569336\n",
              "6    50.0  32.839084\n",
              "7    14.3  16.535597\n",
              "8    20.7  19.026896\n",
              "9    37.6  37.689635"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PN_9VJuj_pe",
        "colab_type": "code",
        "outputId": "d5391412-2119-4d70-d0d1-dfd29323442a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        }
      },
      "source": [
        "prediction = regressor.predict(X_test)\n",
        "plt.scatter(y_test, prediction)\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6d1a57e7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+DnT93nHdqWwCLzIoACBbXGtiN9z48eM1f/58SboqYJ4+fbokqaNj9H+oY7GY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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1200x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gliImHprknbk",
        "colab_type": "code",
        "outputId": "ccc6dac3-1203-4c62-be66-1fda63da4c9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        }
      },
      "source": [
        "evaluate.head(10).plot(kind = 'bar')\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d1a536710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dmoEDB77vfldffXUWLFiQfv365YknnsjEiRMzbdq03HDDDWlsbMxNN92UuXPn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\nYTX+z33lAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlBYofl1PH5q",
        "colab_type": "code",
        "outputId": "760514d3-0813-4fa3-8e72-ee3bd50c8f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Scoring the model\n",
        "from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error\n",
        "\n",
        "# R2 Score\n",
        "print(f\"R2 score: {r2_score(y_test, y_pred)}\")\n",
        "\n",
        "# Mean Absolute Error (MAE)\n",
        "print(f\"MSE score: {mean_absolute_error(y_test, y_pred)}\")\n",
        "\n",
        "# Mean Squared Error (MSE)\n",
        "print(f\"MSE score: {mean_squared_error(y_test, y_pred)}\")"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 score: 0.5383003344910231\n",
            "MSE score: 4.750294229575126\n",
            "MSE score: 45.0773394247183\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4DEBnbCO74s",
        "colab_type": "text"
      },
      "source": [
        "**multiple linear regression**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqzS13p6dKTB",
        "colab_type": "code",
        "outputId": "8b4a95ee-4a74-480a-8bc4-9a9ed001c65e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Preparing the data\n",
        "X = df[['LSTAT','CRIM','NOX','TAX','PTRATIO','CHAS','DIS']]\n",
        "y = df[['MEDV']]\n",
        "\n",
        "# Splitting the dataset into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 10)\n",
        "\n",
        "# Fitting the training data to our model\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxvgXC1_deiu",
        "colab_type": "code",
        "outputId": "db1965bc-6dab-498d-8bd0-b38040c2953c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "regressor.score(X_test, y_test)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6446942534265363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9UYkmY7dt6B",
        "colab_type": "code",
        "outputId": "02d3517a-9437-4a67-868e-cf565796b931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# predict the y values\n",
        "y_pred=regressor.predict(X_test)\n",
        "# a data frame with actual and predicted values of y\n",
        "evaluate = pd.DataFrame({'Actual': y_test.values.flatten(), 'Predicted': y_pred.flatten()})\n",
        "evaluate.head(10)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.4</td>\n",
              "      <td>27.445779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.1</td>\n",
              "      <td>31.364849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.5</td>\n",
              "      <td>30.681874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.6</td>\n",
              "      <td>22.143726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.6</td>\n",
              "      <td>23.063037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.3</td>\n",
              "      <td>16.421246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>50.0</td>\n",
              "      <td>36.733894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14.3</td>\n",
              "      <td>15.887917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>20.7</td>\n",
              "      <td>25.718492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37.6</td>\n",
              "      <td>32.816198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0    28.4  27.445779\n",
              "1    31.1  31.364849\n",
              "2    23.5  30.681874\n",
              "3    26.6  22.143726\n",
              "4    19.6  23.063037\n",
              "5    14.3  16.421246\n",
              "6    50.0  36.733894\n",
              "7    14.3  15.887917\n",
              "8    20.7  25.718492\n",
              "9    37.6  32.816198"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cI3fNnRevdF",
        "colab_type": "text"
      },
      "source": [
        "Let's make another multi linear regression model with more features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4iwN03Be_4u",
        "colab_type": "code",
        "outputId": "68117206-997c-4377-e868-5480806e6d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Preparing the data\n",
        "X = df[['LSTAT','CRIM','NOX','TAX','PTRATIO']]\n",
        "y = df[['MEDV']]\n",
        "\n",
        "# Splitting the dataset into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 10)\n",
        "\n",
        "# Fitting the training data to our model\n",
        "regressor.fit(X_train, y_train)\n",
        "\n",
        "#score of this model\n",
        "regressor.score(X_test, y_test)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5798770784084717"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yAw64TpfRRe",
        "colab_type": "text"
      },
      "source": [
        "Accuracy of this model is 57%. Target variable's data frame for this built model is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQfzLVcafQJ8",
        "colab_type": "code",
        "outputId": "02f9a9c1-a868-4daa-9071-2868266964a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# predict the y values\n",
        "y_pred=regressor.predict(X_test)\n",
        "# a data frame with actual and predicted values of y\n",
        "evaluate = pd.DataFrame({'Actual': y_test.values.flatten(), 'Predicted': y_pred.flatten()})\n",
        "evaluate.head(10)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.4</td>\n",
              "      <td>25.512908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.1</td>\n",
              "      <td>31.496427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23.5</td>\n",
              "      <td>31.260496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.6</td>\n",
              "      <td>26.553401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.6</td>\n",
              "      <td>25.826407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.3</td>\n",
              "      <td>17.589252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>50.0</td>\n",
              "      <td>34.913399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14.3</td>\n",
              "      <td>15.165121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>20.7</td>\n",
              "      <td>21.605243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37.6</td>\n",
              "      <td>31.078599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0    28.4  25.512908\n",
              "1    31.1  31.496427\n",
              "2    23.5  31.260496\n",
              "3    26.6  26.553401\n",
              "4    19.6  25.826407\n",
              "5    14.3  17.589252\n",
              "6    50.0  34.913399\n",
              "7    14.3  15.165121\n",
              "8    20.7  21.605243\n",
              "9    37.6  31.078599"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    }
  ]
}